Overview

This strategy is a dynamic trend breakthrough trading approach based on multi-timeframe pivots and the Relative Strength Index (RSI). By combining weekly-level price support and resistance levels with the RSI indicator, the strategy aims to capture trend opportunities in financial markets while providing refined position management and risk control mechanisms.

Strategy Principles

The core principles of the strategy include the following key steps:

1. Multi-Timeframe Price Pivot Calculation:

• Calculate key support and resistance levels using weekly-level previous candle's close, high, and low prices
• Compute typical support levels (S1, S2, S3) and resistance levels (R1, R2, R3)
• Dynamically adjust support and resistance levels' sensitivity through a factor

2. RSI Indicator Dynamic Optimization:

• Calculate RSI with a 21-period length
• Introduce Exponential Moving Average (EMA) to smooth RSI
• Construct a composite indicator combining raw RSI and EMA smoothed values

3. Trading Signal Generation:

• Long Entry: Composite indicator crosses above 0
• Long Exit: Highest price breaks R3 resistance level
• Short Entry: Lowest price breaks S3 support level
• Short Exit: Composite indicator crosses below 0

Strategy Advantages

1. Multi-Timeframe Perspective: Effectively filter short-term market noise by introducing weekly-level data
2. Flexible Position Management: Staged take-profit mechanism reduces single-trade risk
3. Dynamic Indicator Construction: Combine RSI and EMA to improve signal accuracy
4. Symmetric Long and Short Trading Logic: Provide flexible strategies for different market environments
5. Controllable Risk: Built-in stop-loss and staged take-profit mechanisms

Strategy Risks

1. Indicator Lag: RSI and price pivots may have latency issues
2. Parameter Sensitivity: Strategy performance highly depends on parameter selection
3. Transaction Cost Impact: Frequent trading may lead to high commission fees
4. Extreme Market Conditions: Trend reversal and violent fluctuations may cause strategy failure

Strategy Optimization Directions

1. Introduce machine learning algorithms to optimize parameter selection
2. Add volume and volatility filtering mechanisms
3. Combine more technical indicators for signal verification
4. Develop dynamic stop-loss and take-profit algorithms
5. Introduce more complex position sizing management models

Summary

The strategy builds a relatively robust trend breakthrough trading method through multi-timeframe and multi-indicator comprehensive analysis. Its core advantage lies in dynamically capturing market trends and fine-grained risk management. Future optimization spaces include algorithmic intelligence and iterative risk control model development.